Are there real-world datasets and case studies?

Definitely. You’ll work on social media data, product reviews, crisis event analysis, and Yelp‑style case studies as practical projects.

How is sentiment analysis performed in multiple languages?

Multilingual sentiment analysis is achieved through cross-lingual embeddings and transformer models like mBERT and XLM-R. This course teaches fine-tuning these models to analyze sentiment across various languages without translation.

Who should take this course and what are the prerequisites?

This course is ideal for data scientists, NLP engineers, and ML researchers with basic knowledge of Python, NLP fundamentals, and an interest in multilingual or domain-specific sentiment systems.

Can I use this course for social media sentiment monitoring?

Yes. The course includes use cases such as Twitter sentiment analysis, brand monitoring, and public opinion mining using real-world, multilingual data.

How do I evaluate sentiment models trained in different languages?

The course walks through evaluation strategies using metrics like F1, precision, recall, and confusion matrices, including techniques for multilingual benchmarking.

How is this course different from a basic sentiment analysis tutorial?

Unlike entry-level tutorials, this course dives into subword encoding, cross-lingual model fine-tuning, and aspect-level sentiment extraction using real-world multilingual data and cutting-edge NLP frameworks.

Can I use the skills from this course in industry projects?

Absolutely. The course emphasizes practical skills in tokenization, model deployment, lexicon construction, and multilingual evaluation—ready for use in enterprise NLP, customer feedback systems, and media analytics.

Is this course useful for building multilingual chatbots or AI assistants?

Yes. The advanced tokenization and sentiment techniques taught here can be integrated into chatbots, virtual assistants, and AI-driven customer service tools with multilingual capabilities.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Advanced Tokenization and Sentiment Analysis

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Advanced Tokenization and Sentiment Analysis

This course is part of Mastering NLP: Tokenization, Sentiment Analysis & Neural MT Specialization

Instructor: Edureka

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Build smarter NLP pipelines with advanced tokenization methods like byte-pair encoding, subword units, and streaming-friendly strategies.
Create powerful text representations using character-level, hybrid, and sentence embeddings for real-world search, classification, and clustering.
Learn sentiment analysis with VADER, machine learning models, and transformer-based approaches like BERT and RoBERTa.
Analyze opinion trends, perform aspect-level and multilingual sentiment analysis, and ensure fairness and accuracy in sensitive applications.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

16 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Mastering NLP: Tokenization, Sentiment Analysis & Neural MT Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

This course offers a clear pathway to undertsand advanced tokenization and sentiment analysis—two core pillars of modern NLP. You'll learn how to convert raw text into structured input using subword, character-level, and adaptive tokenization techniques, and how to extract sentiment using rule-based, statistical, and deep learning models.

Through hands-on exercises, you’ll gain the skills to handle complex language input, model sentiment at fine granularity, and deploy systems that generalize across domains and languages. By the end of this course, you will be able to: - Explain and apply advanced tokenization techniques, including BPE, character-level, and streaming methods - Handle out-of-vocabulary terms and domain-specific language using adaptive and hybrid encoding strategies - Build sentiment analysis models using VADER, Naïve Bayes, BERT, and RoBERTa - Address challenges such as class imbalance, multilingual variation, and aspect-level sentiment - Evaluate sentiment systems using semantic similarity, temporal trends, and domain-specific metrics This course is ideal for NLP practitioners, data scientists, developers, and applied researchers aiming to build robust, ethical, and production-ready sentiment analysis systems. A basic understanding of Python, NLP fundamentals, and machine learning is recommended. Join us to learn how tokenization and sentiment analysis power the next generation of intelligent language technologies.

In this module, learners will explore advanced techniques for breaking down and encoding text for machine understanding. They will examine subword, byte-level, and adaptive tokenization methods used in modern NLP models. The module also introduces character-level and hybrid embeddings, as well as sentence embeddings for capturing semantic meaning in tasks like search, classification, and clustering.

What's included

19 videos6 readings5 assignments1 discussion prompt

19 videos Total 89 minutes

Specialization Introduction 5 minutes
Course Introduction 5 minutes
Introduction to Subword Tokenization 5 minutes
Byte-Pair Encoding (BPE) and Unigram Language Models 5 minutes
Handling Out-of-Vocabulary (OOV) Words 4 minutes
Demonstration: Subword Tokenization in Real-World Scenarios 6 minutes
Dynamic Tokenization Strategies 5 minutes
Real-Time Tokenization in Streaming Applications 3 minutes
Tokenization for Low-Resource and Morphologically Rich Languages 4 minutes
Demonstration: OOV Words and Transformer Tokenization (BERT and GPT) 4 minutes
Demonstration: Dynamic and Adaptive Tokenization 5 minutes
Character-Level Embeddings with CNNs and RNNs 5 minutes
FastText: Subword Embeddings and Their Utility 4 minutes
Hybrid Embeddings: Combining Character and Word Representations 4 minutes
Hybrid Models: Character-CNNs Integrated with Transformers 5 minutes
Applications of Character-Level Modeling in NLP Tasks 5 minutes
Sentence-BERT and Universal Sentence Encoder 5 minutes
Techniques for Measuring Semantic Similarity: Cosine, Jaccard, Euclidean 5 minutes
Sentence Embedding Use Cases in Search and Chatbots 5 minutes

6 readings Total 130 minutes

Subword and Byte-Pair Encoding Techniques: A Practical Perspective 20 minutes
Real-Time and Domain-Aware NLP Solutions 20 minutes
Handling the Limits of Word-Level Representations 20 minutes
Sentence Embeddings and Semantic Similarity in Applied NLP 20 minutes
Module Summary: Advanced Tokenization and Text Encoding 20 minutes
From Bytes to Meaning: Tokenization and Embeddings in Multilingual NLP 30 minutes

5 assignments Total 54 minutes

Practice Quiz: Subword and Byte-Pair Encoding Techniques 6 minutes
Practice Quiz: Adaptive and Streaming Tokenization 6 minutes
Practice Quiz: Character-Level and Hybrid Embeddings 6 minutes
Practice Quiz: Sentence Embeddings and Semantic Similarity 6 minutes
Knowledge Check: Advanced Tokenization and Text Encoding 30 minutes

1 discussion prompt Total 10 minutes

Introduce Yourself 10 minutes

In this module, learners will explore the full range of approaches used to analyze sentiment in text, from rule-based lexicons to deep learning with transformer models. They will examine how sentiment is extracted, scored, and classified, and learn how to handle challenges like class imbalance, domain specificity, and low-resource settings. Practical demonstrations will help reinforce the application of models such as VADER, Naïve Bayes, BERT, and RoBERTa in real-world sentiment analysis tasks.

What's included

16 videos5 readings4 assignments

16 videos Total 80 minutes

Introduction to Sentiment Analysis 5 minutes
Rule-Based Techniques and Sentiment Lexicons (VADER, SentiWordNet) 6 minutes
Preprocessing Considerations for Sentiment Analysis Tasks 7 minutes
Lexicon Scoring and Heuristics in Polarity Detection 5 minutes
Demo - Sentiment Analysis Using VADER, SentiWordNet, and Custom Lexicons 6 minutes
Naïve Bayes and Support Vector Machines for Sentiment Classification 5 minutes
Dimensionality Reduction: Non-Negative Matrix Factorization (NMF) 4 minutes
Topic Modeling in Sentiment Tasks: Latent Dirichlet Allocation (LDA) 5 minutes
Handling Imbalanced Sentiment Datasets 5 minutes
Evaluation Metrics and Semantic Measures 4 minutes
LSTMs and GRUs for Sequential Sentiment Modeling 5 minutes
Attention Mechanisms in Deep Sentiment Models 4 minutes
Sentiment Classification with Pretrained BERT Models 5 minutes
Fine-Tuning Transformer Models for Domain-Specific Sentiment Tasks 5 minutes
State-of-the-Art Transformers: RoBERTa, DistilBERT, GPT-Based Approaches 4 minutes
Few-Shot and Zero-Shot Sentiment Classification Using Instruction-Tuned LLMs 5 minutes

5 readings Total 100 minutes

Fundamentals of Sentiment Analysis: Lexicons, Rules, and Preprocessing for Polarity Detection 20 minutes
From Probabilities to Patterns: Classical Machine Learning in Sentiment Analysis 20 minutes
Context, Context, Context: Deep Learning in Sentiment Analysis 20 minutes
Module Summary: Sentiment Analysis – Models, Methods, and Techniques 20 minutes
Analyzing Emotion at Scale: Rule-Based, Classical, and Deep Learning Approaches to Sentiment Analysis 20 minutes

4 assignments Total 48 minutes

Practice Quiz: Fundamentals of Sentiment Analysis 6 minutes
Practice Quiz: Traditional Machine Learning Approaches 6 minutes
Practice Quiz: Deep Learning for Sentiment Analysis 6 minutes
Knowledge Check: Sentiment Analysis – Models, Methods, and Techniques 30 minutes

In this module, learners will examine how sentiment analysis is applied in dynamic, multilingual, and high-impact environments. The lessons focus on tracking sentiment trends over time, extracting aspect-level opinions, and extending sentiment models across languages. Learners will also evaluate the ethical risks of sentiment modeling and explore how to design fair, accountable systems for sensitive applications like healthcare and justice.

What's included

19 videos6 readings5 assignments

19 videos Total 76 minutes

Tracking Sentiment Trends Over Time 4 minutes
Detecting Sudden Shifts in Opinion 3 minutes
Sentiment Analysis for Public Discourse and Crisis Events 3 minutes
Use Cases: Social Media Monitoring, Political Event Analysis 4 minutes
Demonstration: Temporal Sentiment Tracking and Event Impact Analysis 6 minutes
Introduction to ABSA and Fine-Grained Sentiment 3 minutes
Aspect Extraction Using Machine Learning 3 minutes
Aspect-Level Sentiment Classification Techniques 6 minutes
Integrating NER with ABSA for Enhanced Precision 3 minutes
Demonstration: Aspect Based Sentiment Analysis 6 minutes
Challenges in Multilingual Sentiment Modeling 5 minutes
Language-Agnostic Lexicons and Embeddings 3 minutes
Cross-Lingual Embeddings: MUSE, LASER 3 minutes
Fine-Tuning mBERT and XLM-R for Multilingual Tasks 5 minutes
Zero-Shot and Few-Shot Multilingual Sentiment Transfer 3 minutes
Bias in Sentiment Models: Gender, Race, Culture 4 minutes
Reducing False Negatives and Positives in High-Risk Applications 4 minutes
Sentiment Analysis in Sensitive Sectors: Healthcare, Justice, HR 4 minutes
Fairness, Accountability, and Transparency in Sentiment Classification 3 minutes

6 readings Total 120 minutes

Tracking Sentiment in Motion: Temporal and Event-Based Sentiment Analysis 20 minutes
Going Beyond the Stars: Aspect-Based Sentiment Analysis for Fine-Grained Opinion Mining 20 minutes
Across Languages and Borders: Building Sentiment Systems for a Multilingual World 20 minutes
Beyond Accuracy: Ethical and Fair Use of Sentiment Analysis Systems 20 minutes
Module Summary: Real-World Applications and Considerations 20 minutes
Sentiment at Scale: Temporal, Granular, Multilingual, and Ethical Perspectives in Modern Opinion Mining 20 minutes

5 assignments Total 54 minutes

Practice Quiz: Temporal and Event-Based Sentiment Trends 6 minutes
Practice Quiz: Aspect-Based Sentiment Analysis (ABSA) 6 minutes
Practice Quiz: Multilingual and Cross-Lingual Sentiment Analysis 6 minutes
Practice Quiz: Ethical and Fair Use of Sentiment Models 6 minutes
Knowledge Check: Real-World Applications and Considerations 30 minutes

In this final module, learners will consolidate key concepts from the course through a structured summary, a real-world project, and a reflective assignment. The focus is on applying the full range of tokenization and sentiment analysis techniques in practical, domain-relevant scenarios. This module also encourages learners to evaluate their understanding and prepare for real-world NLP tasks by integrating technical knowledge with ethical and contextual awareness.

What's included

1 video1 reading2 assignments1 discussion prompt1 ungraded lab

1 video Total 4 minutes

Course Summary: Tokenization and Sentiment Analysis 4 minutes

1 reading Total 20 minutes

From Tokens to Trends: A Practical Journey Through Modern Sentiment Analysis 20 minutes

2 assignments Total 60 minutes

End Course Knowledge Check: Tokenization and Sentiment Analysis 30 minutes
Designing a Multilingual Sentiment Analysis Strategy 30 minutes

1 discussion prompt Total 10 minutes

Describe your Learning Journey 10 minutes

1 ungraded lab Total 60 minutes

Practice Project: IMDb Sentiment Analysis 60 minutes

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Instructor

Edureka

160 Courses 149,681 learners

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Edureka

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Frequently asked questions

This course provides a deep dive into modern tokenization strategies and sentiment analysis techniques used in multilingual and domain-specific NLP tasks. It explores subword modeling methods like Byte-Pair Encoding (BPE), WordPiece, and SentencePiece, and examines character-level encoding approaches. Learners work with cross-lingual embeddings such as MUSE and LASER, and fine-tune models like mBERT and XLM-R for sentiment classification. The course also covers Aspect-Based Sentiment Analysis (ABSA), lexicon-based methods using VADER and SentiWordNet, and applies these techniques to real-world use cases like social media monitoring, political discourse analysis, and crisis event sentiment tracking.

Learners explore modern tokenization strategies, including Byte-Pair Encoding (BPE), WordPiece, SentencePiece, and character-level encoding, all crucial for subword-level text representation.

Yes. The course emphasizes multilingual and cross-lingual sentiment analysis, using shared subword vocabularies and models like mBERT and XLM-R to handle multiple languages effectively.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.